Understanding DataFrames in Python and Accessing Columns with Unique Values

In this blog post, we’ll explore how to access a list of dataframes, identify columns with only two unique values, and transform values accordingly. We’ll also delve into the nuances of handling NaN (Not a Number) values and string data.

Introduction to DataFrames

A DataFrame is a two-dimensional table of data with rows and columns in Python’s Pandas library. It provides an efficient way to store and manipulate structured data.

import pandas as pd

# Create a sample DataFrame
data = {'Name': ['John', 'Anna', 'Peter', 'Linda'],
        'Age': [28, 24, 35, 32],
        'Country': ['USA', 'UK', 'Australia', 'Germany']}
df = pd.DataFrame(data)

print(df)

Output:

Accessing DataFrames in a List

Suppose we have a list of dataframes (qlst) and want to access each dataframe individually.

import pandas as pd

# Create sample DataFrames
df1 = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df2 = pd.DataFrame({'C': [5, 6], 'D': [7, 8]})

qlst = [df1, df2]

Identifying Columns with Unique Values

We want to identify columns in each dataframe that have only two unique values.

def has_two_unique_values(series):
    return len(series.unique()) == 2

# Apply the function to each column in the first DataFrame
print(df1.applymap(has_two_unique_values))

Output:

Transforming Values

We want to transform NaN values in zeros and string values in ones.

def binary_transform(series):
    return series.notna().astype(int)

# Apply the function to each column in the first DataFrame
print(df1.applymap(binary_transform))

Output:

Correcting the Original Code

The original code had a flaw in that it only transformed NaN values to zeros, but not string values. We’ve corrected this by using notna() and astype(int) to convert boolean values to integers.

def binary(x):
    for df in x:
        for column in df:
            qarray = df[column].unique()
            for i in qarray:
                if len(qarray) == 2:
                    df[column] = df[column].notna().astype(int)

Example Usage

qlst = [df1, df2]
binary(qlst)

print(qlst[0])
# Output:
#     A   B
# 0  1   0
# 1  0   1

print(qlst[1])
# Output:
#    C   D
# 0  1   1
# 1  1   1

In conclusion, we’ve explored how to access a list of dataframes, identify columns with unique values, and transform values accordingly. We’ve also corrected the original code to handle string values correctly.

Further Reading

• Pandas Documentation
• Python Pandas Tutorial

By following this tutorial, you’ll gain a deeper understanding of DataFrames in Python and how to manipulate them efficiently.

Last modified on 2024-06-25